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2023 Vol.22, Issue 2 Preview Page

Research Article

A Study on the Optimal Search Keyword Extraction and Retrieval Technique Generation Using Word Embedding

워드 임베딩(Word Embedding)을 활용한 최적의 키워드 추출 및 검색 방법 연구

Jeong-In Lee1
,
Jin-Hee Ahn2
,
Kyung-Taek Koh3
,
YoungSeok Kim4*
이 정인1
,
안 진희2
,
고 경택3
,
김 영석4*
1Member, Researcher, Korean Peninsula Infrastructure Special Committee, Korea Institute of Civil Engineering and Building Technology, 283 Goyangdae-Ro, Ilsanseo-Gu, Gyeonggi-Do 10223, Republic of Korea
2Non-Member, Researcher, Korean Peninsula Infrastructure Special Committee, Korea Institute of Civil Engineering and Building Technology, 283 Goyangdae-Ro, Ilsanseo-Gu, Gyeonggi-Do 10223, Republic of Korea
3Non-Member, Senior Research Fellow, Korean Peninsula Infrastructure Special Committee, Korea Institute of Civil Engineering and Building Technology, 283 Goyangdae-Ro, Ilsanseo-Gu, Gyeonggi-Do 10223, Republic of Korea
4Member, Senior Research Fellow, Northern Infrastructure Specialized Team, Korea Institute of Civil Engineering and Building Technology, 283 Goyangdae-Ro, Ilsanseo-Gu, Gyeonggi-Do 10223, Republic of Korea
*Corresponding Author
30 June 2023. pp. 47-54
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Abstract

In this paper, we propose the technique of optimal search keyword extraction and retrieval for news article classification. The proposed technique was verified as an example of identifying trends related to North Korean construction. A representative Korean media platform, BigKinds, was used to select sample articles and extract keywords. The extracted keywords were vectorized using word embedding and based on this, the similarity between the extracted keywords was examined through cosine similarity. In addition, words with a similarity of 0.5 or higher were clustered based on the top 10 frequencies. Each cluster was formed as ‘OR’ between keywords inside the cluster and ‘AND’ between clusters according to the search form of the BigKinds. As a result of the in-depth analysis, it was confirmed that meaningful articles appropriate for the original purpose were extracted. This paper is significant in that it is possible to classify news articles suitable for the user’s specific purpose without modifying the existing classification system and search form.

Keywords
Keyword extraction
Retrieval technique
Word embedding
Cosine similarity
BigKinds

본 논문에서는 자료 조사를 위한 최적의 키워드 추출 및 검색 방법을 제안하였으며, 북한 건설 관련 동향 파악을 예시로 제안 방법을 검증하였다. 대표적인 국내 언론 플랫폼인 빅카인즈(BigKinds)를 활용하여 표본 기사를 선정하고 키워드를 추출하였다. 추출된 키워드는 워드 임베딩(Word Embedding)을 활용하여 벡터화하였으며, 이를 토대로 코사인 유사도(Cosine Similarity)를 통해 추출된 키워드 간의 유사도를 검사하였다. 또한 상위 빈도수 10개에 대한 키워드를 기준으로 유사도 0.5 이상인 키워드들을 군집화하였다. 각 군집들은 빅카인즈 검색 양식에 맞추어 군집 내부 키워드 간에는 ‘OR’, 군집 간에는 ‘AND’로 형성하였다. 심층 분석 결과, 본래 목적에 맞는 유의미한 기사들이 추출되었음을 확인할 수 있었다. 기존의 분류체계 및 검색 양식을 변형시키지 않은 상태에서 사용자의 세부 목적을 충족시키는 자료 조사·분류가 가능하게 되었다는 점에서 의의를 갖는다.

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Information
  • Publisher :Korean Geosythetics Society
  • Publisher(Ko) :한국지반신소재학회
  • Journal Title :Journal of the Korean Geosynthetics Society
  • Journal Title(Ko) :한국지반신소재학회 논문집
  • Volume : 22
  • No :2
  • Pages :47-54
  • Received Date : 2023-06-05
  • Revised Date : 2023-06-13
  • Accepted Date : 2023-06-19
  • DOI :https://doi.org/10.12814/jkgss.2023.22.2.047
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